Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures论文翻译/笔记

Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures论文翻译/笔记_weixin_43998119的博客-CSDN博客原文链接毕业论文主题是研究本文提出的APoZ方法应用于ResNet剪枝时的效果，特此精读目录摘要一，介绍二，相关工作三，零激活和网络剪枝3.1 VGG-16上的零激活3.2 网络剪枝和重训练四，实验摘要最前沿的神经网络正在变得更深且宽，虽然它们的表现随着层和神经元数量的增加而提高，但是设计一个对算力和存储要求不高的高效网络却并不简单，因为这意味着大量的实验和微调。这篇论文介绍了一种基于根据神经元对于某个大型数据集的输出来评估其重要性，并据此剔除不重要的神经元，进行微调这一迭代过程的剪枝算法。这个算法https://blog.csdn.net/weixin_43998119/article/details/115779336

代码链接：

GitHub - jjeamin/Network_Trimming_Pytorch: Implementation network trimming using pytorchImplementation network trimming using pytorch. Contribute to jjeamin/Network_Trimming_Pytorch development by creating an account on GitHub.https://github.com/jjeamin/Network_Trimming_Pytorch

https://github.com/rezabonyadi/deep_networks_trimminghttps://github.com/rezabonyadi/deep_networks_trimming

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Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures论文翻译/笔记

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